Show the code
library(haven)
library(foreign)
library(readr)
library(dplyr)
library(ggplot2)
library(foreign)
#library(pacman)
#pacman ::(haven, foreign, readr, dplyr, ggplot2, foreign, tidyverse, rio, pander)Show the code
german_conscr = read_dta("germanconscr.dta")
# bdec - birth year
# "height" - cm
# all M
german_conscr# A tibble: 1,382 × 5
gebger bdec height age co
<chr> <dbl> <dbl> <dbl> <chr>
1 brueckenau 1850 170. 21 de-se
2 brueckenau 1850 156. 21 de-se
3 brueckenau 1850 172. 21 de-se
4 brueckenau 1850 168. 21 de-se
5 brueckenau 1850 167. 21 de-se
6 brueckenau 1850 160. 21 de-se
7 brueckenau 1850 161. 21 de-se
8 brueckenau 1850 162. 21 de-se
9 brueckenau 1850 162. 21 de-se
10 brueckenau 1850 170. 21 de-se
# ℹ 1,372 more rowsShow the code
german_prison = read_dta("germanprison.dta")
#"bdec" - birth year
#"height" - cm
# all M
german_prison# A tibble: 477 × 4
age bdec co height
<dbl> <dbl> <chr> <dbl>
1 22 1850 de 156
2 26 1850 de 168
3 26 1850 de 161
4 25 1850 de 159
5 26 1850 de 175
6 24 1850 de 173
7 24 1850 de 173
8 24 1850 de 172
9 24 1850 de 161
10 21 1850 de 175
# ℹ 467 more rowsShow the code
Heights_south_east <- read.dbf("Heights_south-east/B6090.dbf")#Birth Year: "ALTER"
#Height: "CMETER"
#sex: "F" 5 = m, 6 = f
head(Heights_south_east) RECNO SJ REG GEBGER SL GEBJZ GEWERBE BERUF STD_ELT F Z V S GEBJ CMETER
1 3669 1773 2 lau 0 173 <NA> 0 <NA> 6 1 0 0 1733 177.55
2 3670 1773 2 ma 0 173 <NA> 0 <NA> 5 8 0 0 1737 165.39
3 3671 1773 2 bre 0 175 <NA> 0 <NA> 5 11 0 0 1750 172.68
4 3672 1773 2 neu 0 173 <NA> 0 <NA> 5 11 0 0 1734 172.68
5 3673 1773 2 alz 0 173 <NA> 0 <NA> 6 0 2 0 1734 176.33
6 3674 1773 2 hd 0 173 <NA> 0 <NA> 5 6 0 0 1737 160.52
MASS ALTER ALTERGR ARMGATT ZOLLABRD ZOLLVRD KOM2 REGIM COMP GREN LEIB OFF1
1 v 40 6 0 73 73.0 B03H18 06.ir riedl 0 0 1
2 v 36 6 0 68 68.0 B03H18 06.ir riedl 0 0 1
3 v 23 6 0 71 71.0 B03H18 06.ir riedl 0 0 0
4 v 39 6 0 71 71.0 B03H18 06.ir riedl 0 0 0
5 v 39 6 0 72 72.5 B03H18 06.ir riedl 0 0 0
6 v 36 6 0 66 66.0 B03H18 06.ir riedl 0 0 0
OFF2 OPF PF A16 A17 A18 A19 A20 A21 A22 A50 G2529 G3034 G3539 G4044 G4549
1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0
2 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0
3 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
4 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0
5 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0
6 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0
G5054 G5559 G6064 G6569 G7074 COMPOS
1 0 0 0 0 0 1
2 0 0 0 0 0 2
3 1 0 0 0 0 3
4 0 0 0 0 0 4
5 0 0 0 0 0 5
6 0 0 0 0 0 6Show the code
BLS = read_csv("https://raw.githubusercontent.com/hadley/r4ds/main/data/heights.csv")
#"height" in
#sex (male, female)
#birthday all: 1950
BLS# A tibble: 1,192 × 6
earn height sex ed age race
<dbl> <dbl> <chr> <dbl> <dbl> <chr>
1 50000 74.4 male 16 45 white
2 60000 65.5 female 16 58 white
3 30000 63.6 female 16 29 white
4 50000 63.1 female 16 91 other
5 51000 63.4 female 17 39 white
6 9000 64.4 female 15 26 white
7 29000 61.7 female 12 49 white
8 32000 72.7 male 17 46 white
9 2000 72.0 male 15 21 hispanic
10 27000 72.2 male 12 26 white
# ℹ 1,182 more rowsShow the code
UWNSD = read_sav("main05022005.sav")
#RT216I height of respondent, inches
#doby DATE OF BIRTH, YEAR
#RE35 male = 1, female = 2, other(drop/na)
UWNSD# A tibble: 7,277 × 2,317
CASE TYPE CASEID CASENUM DOBM DOBY RT1A1 RT1A2 RT1A3 RT1B1
<chr> <chr> <chr> <dbl> <dbl+lb> <dbl> <dbl+lb> <dbl+lb> <dbl+lb> <dbl+lb>
1 00008 S 00008S 8 10 [OCT… 15 5 NA NA 3
2 00058 R 00058R 58 2 [FEB… 40 3 0 [NON… 1 2
3 00058 S 00058S 58 2 [FEB… 40 0 [NON… NA NA 0 [NON…
4 00079 S 00079S 79 11 [NOV… 12 7 NA NA 0 [NON…
5 00082 S 00082S 82 7 [JUL… 61 4 NA 1 4
6 00120 R 00120R 120 10 [OCT… 54 14 0 [NON… 0 [NON… 7
7 00120 S 00120S 120 11 [NOV… 54 1 10 2 0 [NON…
8 00133 R 00133R 133 10 [OCT… 51 18 0 [NON… 3 18
9 00146 R 00146R 146 4 [APR… 52 1 0 [NON… NA 2
10 00146 S 00146S 146 4 [APR… 51 0 [NON… 20 NA 0 [NON…
# ℹ 7,267 more rows
# ℹ 2,307 more variables: RT1B2 <dbl+lbl>, RT1B3 <dbl+lbl>, RT1C1 <dbl+lbl>,
# RT1C2 <dbl+lbl>, RT1C3 <dbl+lbl>, RT1D1 <dbl+lbl>, RT1D2 <dbl+lbl>,
# RT1D3 <dbl+lbl>, RT1E1 <dbl+lbl>, RT1E2 <dbl+lbl>, RT1E3 <dbl+lbl>,
# RT1F1 <dbl+lbl>, RT1F2 <dbl+lbl>, RT1F3 <dbl+lbl>, RT1G1 <dbl+lbl>,
# RT1G2 <dbl+lbl>, RT1G3 <dbl+lbl>, RT1H1 <dbl+lbl>, RT1H2 <dbl+lbl>,
# RT1H3 <dbl+lbl>, RT1I1 <dbl+lbl>, RT1I2 <dbl+lbl>, RT1I3 <dbl+lbl>, …Show the code
# colnames(Heights_south_east)Show the code
# Wrangle datasets
german_conscr <- german_conscr %>%
mutate(birth_year = bdec, height.cm = height, height.in = height / 2.54, study = "German Conscripts") %>%
select(birth_year, height.in, height.cm, study)
german_prison <- german_prison %>%
mutate(birth_year = bdec, height.cm = height, height.in = height / 2.54, study = "German Prison") %>%
select(birth_year, height.in, height.cm, study)Show the code
library(dplyr)
# Update Heights_south_east dataset
Heights_south_east <- Heights_south_east %>%
mutate(
birth_year = 1800 + ALTER,
height.cm = CMETER,
height.in = CMETER / 2.54,
study = "Heights South-East"
) %>%
select(birth_year, height.in, height.cm, study)
head(Heights_south_east) birth_year height.in height.cm study
1 1840 69.90157 177.55 Heights South-East
2 1836 65.11417 165.39 Heights South-East
3 1823 67.98425 172.68 Heights South-East
4 1839 67.98425 172.68 Heights South-East
5 1839 69.42126 176.33 Heights South-East
6 1836 63.19685 160.52 Heights South-EastShow the code
BLS <- BLS %>%
filter(sex == "male") %>%
mutate(birth_year = 1950, height.in = height, height.cm = height * 2.54, study = "BLS") %>%
select(birth_year, height.in, height.cm, study)Show the code
# RT216F (feet)
# RT216I (inches)
UWNSD_FI <- UWNSD %>%
filter(
RT216F != "-1" & RT216F != "-2" & RT216F != "." &
RT216I != "-1" & RT216I != "-2" & RT216I != "25" &
RT216I != "35" & RT216I != "45" & RT216I != "50" & RT216I != "85"
) %>%
mutate(height_in = as.numeric(RT216F) * 12 + as.numeric(RT216I))Show the code
library(dplyr)
UWNSD <- UWNSD_FI %>%
mutate(
birth_year = DOBY,
height.in = height_in,
height.cm = height_in * 2.54,
study = "UWNSD"
) %>%
select(birth_year, height.in, height.cm, study)
#print(UWNSD)
# include the full year
UWNSD <- UWNSD %>%
mutate(
birth_year = ifelse(birth_year <= 5, 2000 + birth_year, 1900 + birth_year)
)
print(UWNSD)# A tibble: 7,255 × 4
birth_year height.in height.cm study
<dbl> <dbl> <dbl> <chr>
1 1940 60 152. UWNSD
2 1940 67 170. UWNSD
3 1912 65 165. UWNSD
4 1961 66 168. UWNSD
5 1954 62 157. UWNSD
6 1954 71 180. UWNSD
7 1951 64 163. UWNSD
8 1952 67 170. UWNSD
9 1951 69 175. UWNSD
10 1949 64 163. UWNSD
# ℹ 7,245 more rowsShow the code
# Combine datasets
combined_data <- bind_rows(german_conscr, german_prison, Heights_south_east, BLS, UWNSD)
library(ggplot2)
library(plotly)
plot_1 <- ggplot(combined_data, aes(x = birth_year, y = height.cm, color = study)) +
geom_point() +
labs(title = "Height Distribution Across Studies",
x = "Birth Year", y = "Height (cm)") +
theme_minimal()
ggplotly(plot_1)Show the code
german_conscr# A tibble: 1,382 × 4
birth_year height.in height.cm study
<dbl> <dbl> <dbl> <chr>
1 1850 66.8 170. German Conscripts
2 1850 61.6 156. German Conscripts
3 1850 67.9 172. German Conscripts
4 1850 66.2 168. German Conscripts
5 1850 65.6 167. German Conscripts
6 1850 63.1 160. German Conscripts
7 1850 63.2 161. German Conscripts
8 1850 63.9 162. German Conscripts
9 1850 63.7 162. German Conscripts
10 1850 67.0 170. German Conscripts
# ℹ 1,372 more rowsShow the code
german_prison# A tibble: 477 × 4
birth_year height.in height.cm study
<dbl> <dbl> <dbl> <chr>
1 1850 61.4 156 German Prison
2 1850 66.1 168 German Prison
3 1850 63.4 161 German Prison
4 1850 62.6 159 German Prison
5 1850 68.9 175 German Prison
6 1850 68.1 173 German Prison
7 1850 68.1 173 German Prison
8 1850 67.7 172 German Prison
9 1850 63.4 161 German Prison
10 1850 68.9 175 German Prison
# ℹ 467 more rowsShow the code
head(Heights_south_east) birth_year height.in height.cm study
1 1840 69.90157 177.55 Heights South-East
2 1836 65.11417 165.39 Heights South-East
3 1823 67.98425 172.68 Heights South-East
4 1839 67.98425 172.68 Heights South-East
5 1839 69.42126 176.33 Heights South-East
6 1836 63.19685 160.52 Heights South-EastShow the code
BLS# A tibble: 505 × 4
birth_year height.in height.cm study
<dbl> <dbl> <dbl> <chr>
1 1950 74.4 189. BLS
2 1950 72.7 185. BLS
3 1950 72.0 183. BLS
4 1950 72.2 183. BLS
5 1950 69.5 177. BLS
6 1950 68.0 173. BLS
7 1950 67.6 172. BLS
8 1950 67.8 172. BLS
9 1950 69.6 177. BLS
10 1950 73.1 186. BLS
# ℹ 495 more rowsShow the code
UWNSD# A tibble: 7,255 × 4
birth_year height.in height.cm study
<dbl> <dbl> <dbl> <chr>
1 1940 60 152. UWNSD
2 1940 67 170. UWNSD
3 1912 65 165. UWNSD
4 1961 66 168. UWNSD
5 1954 62 157. UWNSD
6 1954 71 180. UWNSD
7 1951 64 163. UWNSD
8 1952 67 170. UWNSD
9 1951 69 175. UWNSD
10 1949 64 163. UWNSD
# ℹ 7,245 more rowsShow the code
combined_data# A tibble: 27,866 × 4
birth_year height.in height.cm study
<dbl> <dbl> <dbl> <chr>
1 1850 66.8 170. German Conscripts
2 1850 61.6 156. German Conscripts
3 1850 67.9 172. German Conscripts
4 1850 66.2 168. German Conscripts
5 1850 65.6 167. German Conscripts
6 1850 63.1 160. German Conscripts
7 1850 63.2 161. German Conscripts
8 1850 63.9 162. German Conscripts
9 1850 63.7 162. German Conscripts
10 1850 67.0 170. German Conscripts
# ℹ 27,856 more rowsParagraph on Data Wrangling
The data wrangling process involved importing five datasets in various formats, including Stata, DBF, CSV, and SPSS. Each dataset required careful cleaning, standardizing column names, and converting heights between inches and centimeters. For datasets that did not have a clear birth year, assumptions were made (e.g., for the BLS data, a birth year of 1950 was assumed). One challenge was identifying the birth year in the German soldier data, where column names were in German. After using Google Translate, the correct column (geburtsjahr) was identified. Overall, I had to ensure consistency in column names (birth_year, height.in, height.cm, and study) before combining the datasets.
Analysis and Comparison
The data suggests differences in average heights across birth years and geographic regions. While the datasets show an increase in height over time, especially in 20th-century data, there are variations. For example, the German conscripts from the 19th century appear shorter than those in the mid-20th century BLS data. This aligns with historical trends showing a gradual increase in human height due to better nutrition, health, and living conditions.
In comparison to the previous task, this analysis confirms the trend that humans have become taller over the centuries, but the rate of growth and regional differences highlight the complexity of height distribution. Some discrepancies could arise due to differences in data collection methods, sample populations, or societal conditions during the studied periods. Conclusion
Overall, the data supports the notion that humans have been getting taller over time, though the extent of height increase varies based on geography and historical context. This increase likely results from improved nutrition and health care over time. However, it’s important to consider the limitations of historical datasets and the potential biases that may affect this conclusion.